Overview
You're a BDR on the Enterprise Strategic Expansion team at Fivetran, a data pipeline automation platform. Your job is to prospect into existing Fivetran customer accounts - companies already using the product - to find new stakeholders, departments, or use cases where Fivetran could expand. You book meetings for Account Executives who handle the actual sales cycle.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | Expansion-focused BDR (prospecting into existing accounts) |
| Sales Motion | Outbound-heavy, account-based expansion |
| Deal Complexity | Consultative (data infrastructure decisions) |
| Sales Cycle | N/A - you book meetings, AEs run the cycle (likely 2-4 months) |
| Deal Size | Variable - could be $20K for new connectors or $200K+ for department-wide expansion |
| Quota (est.) | 15-25 qualified meetings per month |
Company Context
Stage: Late-stage (Series D+ or pre-IPO based on 1,770 employee count)
Size: 1,770 employees
Growth: Mature growth stage, building out specialized expansion teams (signal of sophistication)
Market Position: Leader in automated data pipeline space - competing against custom-built solutions, legacy ETL tools, and newer point solutions
GTM Reality
Pipeline Sources:
- 85-90% Outbound - You're running sequences into existing customer accounts, looking for new buyers
- 10-15% Internal referrals - Customer Success might flag expansion opportunities or AEs might request help
- Minimal true inbound - you're not getting MQLs, you're hunting within the install base
SDR/AE Structure: Dedicated expansion BDRs (you) feeding dedicated Enterprise AEs
SE Support: AEs have Solutions Engineers for technical demos once you book the meeting
Competitive Landscape
Main Competitors: Custom-built data pipelines ("we have data engineers"), legacy ETL tools (Informatica, Talend), newer entrants (Airbyte, Stitch - which Talend acquired), cloud-native options (AWS Glue, GCP Dataflow)
How They Differentiate: Pre-built connectors (500+), fully managed service (no infrastructure to maintain), automatic schema drift handling
Common Objections: "Our data team already built this," "Too expensive vs open source," "We're invested in [cloud provider's] native tools"
Win Themes: Speed to value, reliability, frees up data engineering time for higher-value work
What You'll Actually Do
Time Breakdown
Research/List Building (25%) | Outreach (40%) | Follow-up (20%) | Internal Meetings (15%)
Key Activities
- Account mapping: You get assigned existing Fivetran customers (mid-market to enterprise). You research their org structure on LinkedIn, look at tech stack signals, and identify which teams might need more data connectors or expanded usage. Data teams, analytics teams, product teams - anyone who might need to move data.
- Cold outreach within warm accounts: You're calling/emailing people at companies that already use Fivetran, but they personally don't know you. You're saying "Your company uses us in [X department], I'm reaching out because [Y team] might benefit too." It's warmer than pure cold calling but still feels cold to the recipient.
- Running multi-touch sequences: 8-12 touch cadences over 3-4 weeks. Emails, LinkedIn messages, calls. Most people don't respond. You're looking for anyone who'll take a 15-minute call to discuss their data infrastructure needs.
- Qualifying and booking meetings: When someone bites, you do a brief discovery call (10-15 min) to confirm they have a legitimate use case, budget authority or influence, and timing. Then you book them with an AE and hand off notes.
The Honest Reality
What's Hard
- You're calling data engineers, analytics managers, and IT leaders who are busy and didn't ask to hear from you. Even though their company uses Fivetran, they often don't care or don't have budget.
- Navigating large org structures is tedious - figuring out who owns what data pipeline, who has budget, who's actually the decision-maker. LinkedIn stalking gets old.
- Lots of dead ends - you'll find departments that already use Fivetran (CS/marketing didn't tell you), teams with no budget, or people who just ghost after initial interest.
- The expansion motion sounds easier than new business, but you still face rejection daily. People are polite but uninterested most of the time.
What Success Looks Like
- Booking 15-25 qualified meetings per month that AEs accept (they'll reject meetings if qualification is weak)
- 30-40% of your meetings convert to opportunities that AEs actively work
- Consistently hitting activity metrics (50-60 activities per day - calls, emails, LinkedIn touches)
Who You're Selling To
Primary Buyers:
- Data Engineers (IC to Lead level) - they build and maintain pipelines
- Analytics Engineering Managers - they own the data warehouse and BI infrastructure
- VPs of Data/Analytics - they have budget for tools and expansion
- Product Managers (data-intensive products) - sometimes own their own data pipelines
What They Care About:
- Reliability - pipelines can't break, data has to be fresh and accurate
- Engineer time - will this save their team from building/maintaining custom connectors?
- Cost - is this worth the subscription vs doing it ourselves?
- Time to value - how fast can they get new data sources connected?
Requirements
- 1-2 years of BDR/SDR experience (or strong new grad with relevant internships) - they want someone who can handle outbound rejection
- Comfortable with technical buyers - you don't need to be an engineer, but you need to understand data pipelines conceptually
- Account-based prospecting skills - mapping orgs, identifying champions, navigating complex structures
- Resilience - expansion selling has higher connect rates than cold calling, but you'll still hear "no" or get ignored constantly
- Ability to tell a compelling expansion story - "You use Fivetran for X, but did you know Y team could benefit too?"